In order to obtain a social support ecosystem, geo-location based applications now receive so much attention from users and industry alike. Such applications obtain information about a user's mobility using cellular phones and tracking user's location. By tracking the mobility of users, fluctuations of a population in a certain geographic area can be estimated based on counting population migration at the mobile travel behavior server. In order to count population migration at the mobile travel behavior server, these applications review and analyze data generated from the mobile phone, such as control data and user data. These control data and user data generated from the mobile phone are referred to herein as an event data.
Today, a cellular phone is carried and used by a large population. Even while it is not actively used, the cellular phone transmits certain periodic event data to their associated base station (BS) as part of its registration, location area update, and a keep alive operations. These messages are received at the BS, radio network controller, or serving GPRS support node (SGSN) so that the mobile phone can be located at a sector level location at a given time. The mobile network operators, upon collecting such event data from all subscribers, may analyze these data and extract useful information that would help improve society in general and accelerate new business for the corporate customer. Some examples of where mobile travel behavior analysis may help improve society include urban planning, traffic planning, and disaster prevention. As another example, these event data along with some personal attributes such as gender, age etc. of the subscriber may be used for important statistics analysis on population distributions within a given geographical area and time. Conventionally, user statistical distributions are obtained through census surveys, which are typically carried out once every several years.
These event data and personal attributes of the subscribers have been used to realize the above applications to achieve the following objectives 1) obtaining the geographical distribution of subscribers at a given time instant (hourly, daily, weekly, monthly, etc.), and 2) obtaining the flow of population between different geographical area. For the first objective, the goal is to obtain the population in a municipality (or mesh, hexagonal sector, etc.) at a given time of the day. The second objective is used to determine dynamic population migration such as inflow and outflow in a level of municipal, or mesh, or sector or their sojourn time, and their movement distance.
To obtain dynamic population migration, understanding the geographical distribution of subscribers is a challenging task due to the limited information contained in the event data. The event data transmitted by the mobile phone only provides sector-level location information, where the sector size may range from few hundreds of meters to few kilometers. It is not the same as a GPS signal, and it does not include any accurate location even if the mobile phone sends hundreds of transmissions with event data. Accurate mapping of a subscriber's location within a given sector requires non-trivial signal processing techniques that, for example, involve the use of associated BS location information, subscriber's trajectory source/destination position, and estimated trajectory. A second important challenge is that the event data is collected with low frequency. The periodic messages (e.g., periodic location update messages) are transmitted by the user equipment (UE) including communication functions on time intervals that will be on the order of an hour, and the exact frequency of periodic messages can be customized. In some literature, the UE is considered as the mobile phone. While a longer time interval between two periodic messages provides lower messaging overhead and less battery consumption at the UE, it also limits the tracking accuracy of the UE's location.
If the UE is mobile and it crosses the boundary of a location area (LA) that is composed of several sectors, the UE transmits another event data called “location update message (LAU)” to its associated BS which will be located in the next location area. In rural areas, the LA covers a much larger area than one in the urban area, and the number of sectors within the LA is expected to be large; therefore, the mobile travel behavior analysis is mainly composed of periodic message in the rural area due to the absence of LA boundary crossings.
A third example for the event data transmitted by the UE are power-on and power-off messages. Compared to the periodic message and LAU messages, these are less frequently transmitted, but provide sector-level location information for the UE in a way similar to the periodic message and the LAU message. Other examples of the event data messages transmitted by the UE are phone call/receive and SMS message sent/receive.
The prior art related to mobile travel behavior analysis is related to traffic monitoring systems. The prior art discloses identifying the traffic jams and congestion in an online manner using the event data of the UE in a cellular system. The event data are then shared among the users who would like to optimize their travel time with the knowledge of the traffic jam information. In order to estimate traffic jams, a key component that has been developed is to accurately estimate the velocities of the mobile users, sometimes with the help of geographic information & transport network information. However, the prior art does not track individual users' trajectories, and is limited to detecting overall traffic congestions.
The prior art does disclose a method of generating trajectories from mobile phone's data have been discussed. In particular, there exists a general framework for estimating the trajectories from mobile phone's event data. Given the geographic information & transport network information and the location area code (LAC) sequences of the users, one algorithm, referred to as the Needleman-Wunsch algorithm, has been applied to determine the best geographic information & transport network information sequence corresponding to the trajectory samples. The basic goal of this framework is to identify a given estimated LAC trajectory sequence from various possible geographic information & transport network information sequences, and find the best sequence match. However, the algorithm does not consider any information about the two-dimensional distribution of a user's trajectory, the actual physical distances involved between different locations, trip durations, etc. Moreover, a concept of geographical mesh is not used, and the algorithm tries to find trajectories between different LACs. In another method, origin-destination matrices are generated from mobile phone's trajectories has been discussed using the basic framework as in other prior art, and includes the similar limitations.
Some prior art methods of estimating the shortest-path trajectory between the source location and the destination location have been introduced. This prior art includes discussions of possible shortest path algorithms, including the Dijkstra's algorithm, the A* algorithm, and the Dempster-Shafer method. However, typical applications of these methods are online shortest-path route estimation and recommendation to the user for choosing the best path, e.g., for car navigation. Moreover, available location data samples in these works are typically obtained from GPS devices rather than mobile-phone's event data. The GPS information provides accurate location information in a general sense. On the other hand, not all the UEs are equipped with GPS devices. Even if GPS is embedded in the UE, the users may not be comfortable with allowing the GPS information to be used by other entities. Therefore, the usage of GPS information requires additional complexities to protect user's privacy when the location information is transferred from the UEs to the BSs (e.g., network).